ReviewAcute medicine & surgery
Artificial intelligence and machine learning in emergency medicine: a narrative review.
Review in Acute medicine & surgery. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05382000 (Evaluation of an Advanced Triage Tool for Gynecology and Obstetrics Emergencies Based on Artificial Intelligence Algorithms), which is not on this map. Cited by 47 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Evaluation of an Advanced Triage Tool for Gynecology and Obstetrics Emergencies Based on Artificial Intelligence Algorithms
Who cites it
47 citing papers in PubMed, 5 syntheses or guidelines pooled it, 104 citations in OpenAlex.
- Using natural language processing in emergency medicine health service research: A systematic review and meta-analysis.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2024Pooled it
- Applications of Artificial Intelligence and Machine Learning in Emergency Medicine Triage - A Systematic Review.Medical archives (Sarajevo, Bosnia and Herzegovina) · 2024Pooled it
- Detecting Left Heart Failure in Echocardiography through Machine Learning: A Systematic Review.Reviews in cardiovascular medicine · 2022Pooled it
- Influence of artificial intelligence on the work design of emergency department clinicians a systematic literature review.BMC health services research · 2022Pooled it
- The Use of Machine Learning in Emergency Care Units: A Systematic Review.Journal of primary care & community healthPooled it
- Perception and challenges of artificial intelligence (AI) in Emergency Medicine: A multi-country study in Sub-Saharan Africa.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2026Article
- Clinical Applications, Opportunities, and Implementation Challenges of AI in Emergency Medicine: A Narrative Review.Cureus · 2026Review
- Struggling to integrate artificial intelligence in prehospital emergency care in a developing country: exploration of the Iranian experts' views based on qualitative content analysis.International journal of emergency medicine · 2026Article
- Large language models in emergency medicine education: opportunities, challenges, and implementation pathways.Frontiers in public health · 2026Review
- Article
- Development and validation of a prediction model for in-hospital mortality in older adults with acute poisoning.The Journal of international medical research · 2025Article
- A case-specific psychological first aid AI recommendations app for rescue and first responders.European journal of psychotraumatology · 2025Article
- From prediction to action: a retrospective observational study on the real-world implementation of Critical Interventions (CrIs), an AI-based clinical decision support system changing clinical behavior in the emergency department.BMC medical informatics and decision making · 2025Observational
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Tracking inflammation status for improving patient prognosis: A review of current methods, unmet clinical needs and opportunities.Biotechnology advances · 2025Review
- Artificial intelligence applied to electrocardiogram to rule out acute myocardial infarction: the ROMIAE multicentre study.European heart journal · 2025Article
- Artificial intelligence for severity triage based on conversations in an emergency department in Korea.Scientific reports · 2025Article
- Artificial Intelligence in Emergency Medicine: A Primer for the Nonexpert.Journal of the American College of Emergency Physicians open · 2025Article
- Predicting Inpatient Admissions From Emergency Department Triage Using Machine Learning: A Systematic Review.Mayo Clinic proceedings. Digital health · 2025Review
- The role of artificial intelligence in sepsis in the Emergency Department: a narrative review.Annals of translational medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimThe emergence and evolution of artificial intelligence (AI) has generated increasing interest in machine learning applications for health care. Specifically, researchers are grasping the potential of machine learning solutions to enhance the quality of care in emergency medicine.
methodsWe undertook a narrative review of published works on machine learning applications in emergency medicine and provide a synopsis of recent developments.
resultsThis review describes fundamental concepts of machine learning and presents clinical applications for triage, risk stratification specific to disease, medical imaging, and emergency department operations. Additionally, we consider how machine learning models could contribute to the improvement of causal inference in medicine, and to conclude, we discuss barriers to safe implementation of AI.
conclusionWe intend that this review serves as an introduction to AI and machine learning in emergency medicine.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.